Google BigQuery VS SAP Datasphere
As of 30 October 2026, SAP Datasphere is named by 219 companies and Google BigQuery by 2,715; Google BigQuery skews to companies under 200 people (48% vs 19%), SAP Datasphere to companies over 1,000 (64% vs 26%).
As of 30 October 2026 · refreshed daily · counts move as websites, DNS and career sites are re-read.
The website-and-DNS footprint is not comparable between a marketing suite and a CRM: Google BigQuery's script and mail records show on hundreds of thousands of small sites, SAP Datasphere rarely leaves a trace outside its own apps. Compare on the job-posting numbers; use the footprint for reach.
- Google BigQuery: 2–200 employees
- 48%
- SAP Datasphere: 2–200 employees
- 19%
- Google BigQuery: 1,000+ employees
- 26%
- SAP Datasphere: 1,000+ employees
- 64%
- Postings per company, 90 days
- 3.1 · 4.0
- Website or DNS footprint
- 0 · 0
Who chooses which
Share of each tool's companies in every segment. "Leans" marks a gap of more than two points.
| Segment | Google BigQuery | share | SAP Datasphere | share | Leans |
|---|---|---|---|---|---|
| Self-employed | 42 | 2% | 1 | 0% | even |
| 2-10 employees | 218 | 8% | 10 | 5% | Google BigQuery |
| 11-50 employees | 439 | 16% | 12 | 5% | Google BigQuery |
| 51-200 employees | 651 | 24% | 19 | 9% | Google BigQuery |
| 201-500 employees | 393 | 14% | 9 | 4% | Google BigQuery |
| 501-1000 employees | 257 | 9% | 27 | 12% | SAP Datasphere |
| 1001-5000 employees | 361 | 13% | 52 | 24% | SAP Datasphere |
| 5001-10000 employees | 102 | 4% | 19 | 9% | SAP Datasphere |
| 10,001+ employees | 252 | 9% | 70 | 32% | SAP Datasphere |
| Segment | Google BigQuery | share | SAP Datasphere | share | Leans |
|---|---|---|---|---|---|
| Technology & software | 1,279 | 47% | 42 | 19% | Google BigQuery |
| Business & finance services | 582 | 21% | 22 | 10% | Google BigQuery |
| Media & content | 163 | 6% | 3 | 1% | Google BigQuery |
| Manufacturing & industry | 157 | 6% | 91 | 42% | SAP Datasphere |
| Commerce & trade | 145 | 5% | 15 | 7% | even |
| Consumer services & leisure | 79 | 3% | 3 | 1% | even |
| Utilities & infrastructure | 62 | 2% | 13 | 6% | SAP Datasphere |
| Health care | 60 | 2% | 4 | 2% | even |
| Public, community & associations | 45 | 2% | 4 | 2% | even |
| Property & construction | 42 | 2% | 7 | 3% | even |
| Hospitality & travel | 40 | 1% | – | – | outside top 12 |
| Transport, logistics & rental | 35 | 1% | 4 | 2% | even |
| Automotive | – | – | 8 | 4% | outside top 12 |
| Segment | Google BigQuery | share | SAP Datasphere | share | Leans |
|---|---|---|---|---|---|
| United States | 1,633 | 60% | 85 | 39% | Google BigQuery |
| United Kingdom | 240 | 9% | 14 | 6% | Google BigQuery |
| Germany | 114 | 4% | 57 | 26% | SAP Datasphere |
| France | 111 | 4% | 7 | 3% | even |
| India | 59 | 2% | 5 | 2% | even |
| Canada | 52 | 2% | 3 | 1% | even |
| Brazil | 50 | 2% | 3 | 1% | even |
| Netherlands | 43 | 2% | 4 | 2% | even |
| Sweden | 43 | 2% | – | – | outside top 12 |
| Australia | 42 | 2% | – | – | outside top 12 |
| Spain | 38 | 1% | – | – | outside top 12 |
| Poland | 30 | 1% | – | – | outside top 12 |
| Switzerland | – | – | 6 | 3% | outside top 12 |
| Belgium | – | – | 5 | 2% | outside top 12 |
| Austria | – | – | 4 | 2% | outside top 12 |
| Denmark | – | – | 3 | 1% | outside top 12 |
| Segment | Google BigQuery | share | SAP Datasphere | share | Leans |
|---|---|---|---|---|---|
| Data & AI | 5,837 | 53% | 269 | 25% | Google BigQuery |
| Software engineering | 1,678 | 15% | 55 | 5% | Google BigQuery |
| IT operations & business systems | 610 | 6% | 504 | 47% | SAP Datasphere |
| Marketing & communications | 389 | 4% | – | – | outside top 12 |
| Sales | 294 | 3% | 7 | 1% | Google BigQuery |
| Product, program & project management | 258 | 2% | 29 | 3% | even |
| Finance & accounting | 175 | 2% | 15 | 1% | even |
| Logistics warehouse and transport | 134 | 1% | 2 | 0% | even |
| Engineering (non-software) | 87 | 1% | 2 | 0% | even |
| Customer service & success | 74 | 1% | – | – | outside top 12 |
| Business development & strategy | 45 | 0% | – | – | outside top 12 |
| Legal, compliance & risk | 44 | 0% | – | – | outside top 12 |
| Unclassified | – | – | 15 | 1% | outside top 12 |
| General management & consulting | – | – | 5 | 0% | outside top 12 |
| Construction and civil | – | – | 2 | 0% | outside top 12 |
| Skilled trades and field service | – | – | 2 | 0% | outside top 12 |
The companies behind each column
Companies using Google BigQuery →
Companies using SAP Datasphere →
data warehouses olap ranked →
Quote the sentence with the date and the link. The numbers change daily; the link always shows the current ones.
SAP Datasphere is named by 219 companies in job postings and Google BigQuery by 2,715; 0 name both. (Breaknorm, 30 October 2026, https://breaknorm.co/compare/google-bigquery-vs-sap-datasphere)
Read from the source, not self-reported
Every number on this page can be traced to a page we crawled or a posting we read. Full methodology →
- Job postingsRead hourly from career sites. A tool counts when the employer's own text names it as the team's system; both tools can count for one company.
- Website and DNSScripts and embeds on the company's pages, plus SPF, DKIM, MX and CNAME records of its domain that point at the vendor.
- SegmentsCountry, size and industry from the company profile built from its website.
- Not a reviewNo ratings, prices or feature claims. For those, read the vendors.
Google BigQuery vs SAP Datasphere: questions
Which is more used, Google BigQuery or SAP Datasphere?
By companies naming it in their own job postings, Google BigQuery (2,715) over SAP Datasphere (219). By website-and-DNS footprint, SAP Datasphere (0 vs 0), because its scripts and mail records sit on many small company sites.
Who chooses Google BigQuery?
48% of Google BigQuery companies have 2 to 200 employees, against 19% for SAP Datasphere. 26% of Google BigQuery companies have over 1,000 employees, against 64% for SAP Datasphere.
Do companies run both?
0 companies name both in their postings, usually Google BigQuery in marketing and SAP Datasphere in sales, or during a migration.
Where do these numbers come from?
From the employer's own text in job postings read hourly from career sites, and from scripts found on company websites in our crawl. No vendor data, no surveys.
Get either list your way
The same data as a search, over MCP and the API, as bulk files, or pushed to you as it changes.
Every company on either tool,
with the person who owns it
Filter on Google BigQuery or SAP Datasphere. Start free, no card.